Search Results (15)

Smart city operation assumes dynamic infrastructure in various aspects. However, organization and process modelling require domain expertise and significant efforts from modelers. As a result, such processes are still not well supported by IT systems and still mostly remain manual tasks. Today, machine learning technologies are capable of performing various tasks including those that have normally been associated with people; for example, tasks that require creativeness and expertise. Generative adversarial networks (GANs) are a good example of this phenomenon. This paper proposes an approach to generating organizational and process models using a GAN. The proposed GAN architecture takes into account both tacit expert knowledge encoded in the training set sample models and the symbolic knowledge (rules and algebraic constraints) that is an essential part of such models. It also pays separate attention to differentiable functional constraints, since learning those just from samples is not efficient. The approach is illustrated via examples of logistic system modelling and smart tourist trip booking process modelling. The developed framework is implemented in a publicly available open-source library that can potentially be used by developers of modelling software. Full article

This study presents an approach for predicting soil class probabilities by integrating synthetic composite imagery of bare soil with long-term vegetation remote sensing data and soil survey data. The goal is to develop detailed soil maps for the agro-innovation center “Orlovka-AIC” (Samara Region), with a focus on lithological heterogeneity. Satellite data were sourced from a cloud-filtered collection of Landsat 4–5 and 7 images (April–May, 1988–2010) and Landsat 8–9 images (June–August, 2012–2023). Bare soil surfaces were identified using threshold values for NDVI (<0.06), NBR2 (<0.05), and BSI (>0.10). Synthetic bare soil images were generated by calculating the median reflectance values across available spectral bands. Following the adoption of no-till technology in 2012, long-term average NDVI values were additionally calculated to assess the condition of agricultural lands. Seventy-one soil sampling points within “Orlovka-AIC” were classified using both the Russian and WRB soil classification systems. Logistic regression was applied for pixel-based soil class prediction. The model achieved an overall accuracy of 0.85 and a Cohen’s Kappa coefficient of 0.67, demonstrating its reliability in distinguishing the two main soil classes: agrochernozems and agrozems. The resulting soil map provides a robust foundation for sustainable land management practices, including erosion prevention and land use optimization. Full article

The detection of the human mental fatigue state holds immense significance due to its direct impact on work efficiency, specifically in system operation control. Numerous approaches have been proposed to address the challenge of fatigue detection, aiming to identify signs of fatigue and alert the individual. This paper introduces an approach to human mental fatigue assessment based on the application of machine learning techniques to the video of a working operator. For validation purposes, the approach was applied to a dataset, “Human Fatigue Assessment Based on Video Data” (HFAVD) integrating video data with features computed by using our computer vision deep learning models. The incorporated features encompass head movements represented by Euler angles (roll, pitch, and yaw), vital signs (blood pressure, heart rate, oxygen saturation, and respiratory rate), and eye and mouth states (blinking and yawning). The integration of these features eliminates the need for the manual calculation or detection of these parameters, and it obviates the requirement for sensors and external devices, which are commonly employed in existing datasets. The main objective of our work is to advance research in fatigue detection, particularly in work and academic settings. For this reason, we conducted a series of experiments by utilizing machine learning techniques to analyze the dataset and assess the fatigue state based on the features predicted by our models. The results reveal that the random forest technique consistently achieved the highest accuracy and F1-score across all experiments, predominantly exceeding 90%. These findings suggest that random forest is a highly promising technique for this task and prove the strong connection and association among the predicted features used to annotate the videos and the state of fatigue. Full article

The parabolic wave equation describes wave propagation in a preferable direction, which is usually horizontal in underwater acoustics and vertical in seismic applications. For dense receiver arrays (receiver spacing is less than signal wavelength), this equation can be used for propagating the recorded wavefield back into the medium for imaging sources and scattering objects. Similarly, for multiple source and receiver array acquisition, typical for seismic exploration and potentially beneficial for ocean acoustics, one can model data in one run using an extension of the parabolic equation—the pseudo-differential Double Square Root (DSR) equation. This extended equation allows for the modeling and imaging of multi-source data but operates in higher-dimensional space (source, receiver coordinates, and time), which makes its numerical computation time-consuming. In this paper, we apply a faster ray method for solving the DSR equation. We develop algorithms for both kinematic and dynamic ray tracing applicable to either data modeling or true-amplitude recovery. Our results can be used per se or as a basis for the future development of more elaborated asymptotic techniques that provide accurate and computationally feasible results. Full article

Heart rate is an essential vital sign to evaluate human health. Remote heart monitoring using cheaply available devices has become a necessity in the twenty-first century to prevent any unfortunate situation caused by the hectic pace of life. In this paper, we propose a new method based on the transformer architecture with a multi-skip connection biLSTM decoder to estimate heart rate remotely from videos. Our method is based on the skin color variation caused by the change in blood volume in its surface. The presented heart rate estimation framework consists of three main steps: (1) the segmentation of the facial region of interest (ROI) based on the landmarks obtained by 3DDFA; (2) the extraction of the spatial and global features; and (3) the estimation of the heart rate value from the obtained features based on the proposed method. This paper investigates which feature extractor performs better by captioning the change in skin color related to the heart rate as well as the optimal number of frames needed to achieve better accuracy. Experiments were conducted using two publicly available datasets (LGI-PPGI and Vision for Vitals) and our own in-the-wild dataset (12 videos collected by four drivers). The experiments showed that our approach achieved better results than the previously published methods, making it the new state of the art on these datasets. Full article

Driving behaviour analysis has drawn much attention in recent years due to the dramatic increase in the number of traffic accidents and casualties, and based on many studies, there is a relationship between the driving environment or behaviour and the driver’s state. To the best of our knowledge, these studies mostly investigate relationships between one vital sign and the driving circumstances either inside or outside the cabin. Hence, our paper provides an analysis of the correlation between the driver state (vital signs, eye state, and head pose) and both the vehicle maneuver actions (caused by the driver) and external events (carried out by other vehicles or pedestrians), including the proximity to other vehicles. Our methodology employs several models developed in our previous work to estimate respiratory rate, heart rate, blood pressure, oxygen saturation, head pose, eye state from in-cabin videos, and the distance to the nearest vehicle from out-cabin videos. Additionally, new models have been developed using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to classify the external events from out-cabin videos, as well as a Decision Tree classifier to detect the driver’s maneuver using accelerometer and gyroscope sensor data. The dataset used includes synchronized in-cabin/out-cabin videos and sensor data, allowing for the estimation of the driver state, proximity to other vehicles and detection of external events, and driver maneuvers. Therefore, the correlation matrix was calculated between all variables to be analysed. The results indicate that there is a weak correlation connecting both the maneuver action and the overtaking external event on one side and the heart rate and the blood pressure (systolic and diastolic) on the other side. In addition, the findings suggest a correlation between the yaw angle of the head and the overtaking event and a negative correlation between the systolic blood pressure and the distance to the nearest vehicle. Our findings align with our initial hypotheses, particularly concerning the impact of performing a maneuver or experiencing a cautious event, such as overtaking, on heart rate and blood pressure due to the agitation and tension resulting from such events. These results can be the key to implementing a sophisticated safety system aimed at maintaining the driver’s stable state when aggressive external events or maneuvers occur. Full article

One of the most effective vital signs of health conditions is blood pressure. It has such an impact that changes your state from completely relaxed to extremely unpleasant, which makes the task of blood pressure monitoring a main procedure that almost everyone undergoes whenever there is something wrong or suspicious with his/her health condition. The most popular and accurate ways to measure blood pressure are cuff-based, inconvenient, and pricey, but on the bright side, many experimental studies prove that changes in the color intensities of the RGB channels represent variation in the blood that flows beneath the skin, which is strongly related to blood pressure; hence, we present a novel approach to blood pressure estimation based on the analysis of human face video using hybrid deep learning models. We deeply analyzed proposed approaches and methods to develop combinations of state-of-the-art models that were validated by their testing results on the Vision for Vitals (V4V) dataset compared to the performance of other available proposed models. Additionally, we came up with a new metric to evaluate the performance of our models using Pearson’s correlation coefficient between the predicted blood pressure of the subjects and their respiratory rate at each minute, which is provided by our own dataset that includes 60 videos of operators working on personal computers for almost 20 min in each video. Our method provides a cuff-less, fast, and comfortable way to estimate blood pressure with no need for any equipment except the camera of your smartphone. Full article

One of the key functions of driver monitoring systems is the evaluation of the driver’s state, which is a key factor in improving driving safety. Currently, such systems heavily rely on the technology of deep learning, that in turn requires corresponding high-quality datasets to achieve the required level of accuracy. In this paper, we introduce a dataset that includes information about the driver’s state synchronized with the vehicle telemetry data. The dataset contains more than 17.56 million entries obtained from 633 drivers with the following data: the driver drowsiness and distraction states, smartphone-measured vehicle speed and acceleration, data from magnetometer and gyroscope sensors, g-force, lighting level, and smartphone battery level. The proposed dataset can be used for analyzing driver behavior and detecting aggressive driving styles, which can help to reduce accidents and increase safety on the roads. In addition, we applied the K-means clustering algorithm based on the 11 least-correlated features to label the data. The elbow method showed that the optimal number of clusters could be either two or three clusters. We chose to proceed with the three clusters to label the data into three main scenarios: parking and starting driving, driving in the city, and driving on highways. The result of the clustering was then analyzed to see what the most frequent critical actions inside the cabin in each scenario were. According to our analysis, an unfastened seat belt was the most frequent critical case in driving in the city scenario, while drowsiness was more frequent when driving on the highway. Full article

The series of Co(II), Fe(II), and Ni(II) mononuclear coordination compounds of [CoL(NCS)₂]·3DMSO (1), [CoL(H₂O)₂](ClO₄)₂·DMSO (2), [CoL(H₂O)(EtOH)][CoCl₄]·2H₂O (2a), [FeL(NCS)₂]·DMSO (3), and [NiL(NCS)₂]·CH₃CN (4) composition (where L is 2,6-bis(1-(2-(4,6-dimethylpyrimidin-2-yl)hydrazineylidene)ethyl)pyridine), with an [MLA₂] coordination unit (where A is a pair of apical monodentate ligands), was synthesized. In compounds 1, 2, 2a, and 3, the ligand L is pentadentate, and cobalt and iron ions are placed in a heavily distorted pentagonal pyramidal coordination environment, while in 4 the Ni(II) ion is hexacoordinated. Easy plane-type magnetic anisotropy (D = 13.69, 11.46, 19.5, and 6.2 cm⁻¹ for 1, 2, 2a, and 4, respectively) was established for cobalt and nickel compounds, while easy axis-type magnetic anisotropy (D = −14.5 cm⁻¹) was established for iron compound 3. The cobalt coordination compounds 1 and 2 show SIM behavior under a 1500 Oe external magnetic field, with effective magnetization reversal barriers of 65(1) and 60(1) K for 1 and 2, respectively. The combination of Orbach and Raman relaxation mechanisms was shown to adequately describe the temperature dependence of relaxation times for 1 and 2. CASSCF/NEVPT2 calculations were performed to model the parameters of the effective spin Hamiltonian for the compounds under study. Full article

Herein, we report the synthesis, structure and magnetic properties of two mononuclear complexes of general formula [Dy(acac)₃(L)], where L = 2,2-dimethyl-1,3-dioxolo[4,5-f][1,10] phenanthroline (1) or 1,10-phenanthroline-5,6-dione (2), and acac⁻ = acetylacetonate anion. A distorted square-antiprismatic N₂O₆ environment around the central Dy(III) ion is formed by three acetylacetonate anions and a phenanthroline-type ligand. Both complexes display a single-molecule magnet (SMM) behavior at zero applied magnetic field. Modification of the peripheral part of ligands L provide substantial effects both on the magnetic relaxation barrier U_eff and on the quantum tunneling of magnetization (QTM). Ab initio quantum-chemical calculations are used to analyze the electronic structure and magnetic properties. Full article

Developing a driver monitoring system that can assess the driver’s state is a prerequisite and a key to improving the road safety. With the success of deep learning, such systems can achieve a high accuracy if corresponding high-quality datasets are available. In this paper, we introduce DriverMVT (Driver Monitoring dataset with Videos and Telemetry). The dataset contains information about the driver head pose, heart rate, and driver behaviour inside the cabin like drowsiness and unfastened belt. This dataset can be used to train and evaluate deep learning models to estimate the driver’s health state, mental state, concentration level, and his/her activity in the cabin. Developing such systems that can alert the driver in case of drowsiness or distraction can reduce the number of accidents and increase the safety on the road. The dataset contains 1506 videos for 9 different drivers (7 males and 2 females) with total number of frames equal 5119k and total time over 36 h. In addition, evaluated the dataset with multi-task temporal shift convolutional attention network (MTTS-CAN) algorithm. The algorithm mean average error on our dataset is 16.375 heartbeats per minute. Full article

This paper presents an approach and a case study for threat detection during human–computer interaction, using the example of driver–vehicle interaction. We analyzed a driver monitoring system and identified two types of users: the driver and the operator. The proposed approach detects possible threats for the driver. We present a method for threat detection during human–system interactions that generalizes potential threats, as well as approaches for their detection. The originality of the method is that we frame the problem of threat detection in a holistic way: we build on the driver–ITS system analysis and generalize existing methods for driver state analysis into a threat detection method covering the identified threats. The developed reference model of the operator–computer interaction interface shows how the driver monitoring process is organized, and what information can be processed automatically, and what information related to the driver behavior has to be processed manually. In addition, the interface reference model includes mechanisms for operator behavior monitoring. We present experiments that included 14 drivers, as a case study. The experiments illustrated how the operator monitors and processes the information from the driver monitoring system. Based on the case study, we clarified that when the driver monitoring system detected the threats in the cabin and notified drivers about them, the number of threats was significantly decreased. Full article

Bacterial nanocellulose (BNC) is a unique product of microbiological synthesis, having a lot of applications among which the most important is biomedicine. Objective complexities in scaling up the biosynthesis of BNC are associated with the nature of microbial producers for which BNC is not the target metabolite, therefore biosynthesis lasts long, with the BNC yield being small. Thus, the BNC scale-up problem has not yet been overcome. Here we performed biosynthesis of three scaled sheets of BNC (each having a surface area of 29,400 cm², a container volume of 441 L, and a nutrient medium volume of 260 L and characterized them. The static biosynthesis of BNC in a semisynthetic nutrient medium was scaled up using the Medusomyces gisevii Sa-12 symbiotic culture. The experiment was run in duplicate. The BNC pellicle was removed once from the nutrient medium in the first experiment and twice in the second experiment, in which case the inoculum and glucose were not additionally added to the medium. The resultant BNC sheets were characterized by scanning electron microscopy, capillary viscosimetry, infrared spectroscopy, thermomechanical and thermogravimetric analyses. When the nutrient medium was scaled up from 0.1 to 260 L, the elastic modulus of BNC samples increased tenfold and the degree of polymerization 2.5-fold. Besides, we demonstrated that scaled BNC sheets could be removed at least twice from one volume of the nutrient medium, with the yield and quality of BNC remaining the same. Consequently, the world’s largest BNC sheets 210 cm long and 140 cm wide, having a surface area of 29,400 cm² each (weighing 16.24 to 17.04 kg), have been obtained in which an adult with burns or vast wounds can easily be wrapped. The resultant sheets exhibit a typical architecture of cellulosic fibers that form a spatial 3D structure which refers to individual and extremely important characteristics of BNC. Here we thus demonstrated the scale-up of biosynthesis of BNC with improved properties, and this result was achieved by using the symbiotic culture. Full article

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application. Full article

This paper presents an analysis of modern research related to potential threats in a vehicle cabin, which is based on situation monitoring during vehicle control and the interaction of the driver with intelligent transportation systems (ITS). In the modern world, such systems enable the detection of potentially dangerous situations on the road, reducing accident probability. However, at the same time, such systems increase vulnerabilities in vehicles and can be sources of different threats. In this paper, we consider the primary information flows between the driver, vehicle, and infrastructure in modern ITS, and identify possible threats related to these entities. We define threat classes related to vehicle control and discuss which of them can be detected by smartphone sensors. We present a case study that supports our findings and shows the main use cases for threat identification using smartphone sensors: Drowsiness, distraction, unfastened belt, eating, drinking, and smartphone use. Full article